❗️ JAY HELPS EVERYONE EARN MONEY!$29,000 HE'S GIVING AWAY TODAY!

Everyone can join his channel and make money! He gives away from $200 to $5.000 every day in his channel

https://news.1rj.ru/str/+LgzKy2hA4eY0YWNl

⚡️FREE ONLY FOR THE FIRST 500 SUBSCRIBERS! FURTHER ENTRY IS PAID! 👆👇

https://news.1rj.ru/str/+LgzKy2hA4eY0YWNl

1. Install the necessary Python Libraries

Run the following commands from your terminal to install the required libraries:

2. Import necessary libraries

• Streamlit for the web interface
• asyncio for asynchronous operations
• Together AI for LLM interactions

3. Set up the Streamlit app and API key input.

• Creates a noscript for the app
• Adds a secure input field for the Together API key

4. Initialize Together AI clients.

• Sets up Together API key as an environment variable
• Initializes both synchronous and asynchronous Together clients

5. Define the models and aggregator system prompt.

• Specifies the LLMs to be used for generating responses
• Defines the aggregator model and its system prompt

6. Implement the LLM call function.

• Asynchronously calls the LLM with the user's prompt
• Returns the model name and its response

7. Define the main function to run all LLMs and aggregate results.

• Runs all reference models asynchronously
• Displays individual responses in expandable sections
• Aggregates responses using the aggregator model
• Streams the aggregated response.

8. Set up the user interface and trigger the main function.

• Provides an input field for the user's question
• Triggers the main function when the user clicks "Get Answer"

🐍 Tip of the day for experienced Python developers

📌 Use decorators with parameters — a powerful technique for logging, control, caching, and custom checks.

Example: a logger that can set the logging level with an argument:

import functools
import logging

def log(level=logging.INFO):
   def decorator(func):
       @functools.wraps(func)
       def wrapper(*args, **kwargs):
           logging.log(level, f"Call {func.__name__} with args={args}, kwargs={kwargs}")
           return func(*args, **kwargs)
       return wrapper
   return decorator

@log(logging. DEBUG)
def compute(x, y):
   return x + y

✅ Why you need it:

The decorator is flexibly adjustable;

Suitable for prod tracing and debugging in maiden;

Retains the signature and docstring thanks to @functools.wraps.

⚠️ Tip: avoid nesting >2 levels and always write tests for decorator behavior.

Python gives you tools that look like magic, but work stably if you know how to use them.

Topic: Python Classes and Objects — Basics of Object-Oriented Programming

Python supports object-oriented programming (OOP), allowing you to model real-world entities using classes and objects.

---

Defining a Class

class Person:
    def __init__(self, name, age):
        self.name = name
        self.age = age

    def greet(self):
        print(f"Hello, my name is {self.name} and I am {self.age} years old.")

---

Creating Objects

person1 = Person("Alice", 30)
person1.greet()  # Output: Hello, my name is Alice and I am 30 years old.

---

Key Concepts

• Class: Blueprint for creating objects.

• Object: Instance of a class.

• __init__ method: Constructor that initializes object attributes.

• self parameter: Refers to the current object instance.

---

Adding Methods

class Circle:
    def __init__(self, radius):
        self.radius = radius

    def area(self):
        return 3.1416 * self.radius ** 2

circle = Circle(5)
print(circle.area())  # Output: 78.54

---

Inheritance

• Allows a class to inherit attributes and methods from another class.

class Animal:
    def speak(self):
        print("Animal speaks")

class Dog(Animal):
    def speak(self):
        print("Woof!")

dog = Dog()
dog.speak()  # Output: Woof!

---

Summary

• Classes and objects are core to Python OOP.

• Use class keyword to define classes.

• Initialize attrinitith __init__ method.

• Objects are instances of classes.

• Inheritance enables code reuse and polymorphism.

---

#Python #OOP #Classes #Objects #ProgrammingConcepts

Topic: Python Exception Handling — Managing Errors Gracefully

---

Why Handle Exceptions?

• To prevent your program from crashing unexpectedly.

• To provide meaningful error messages or recovery actions.

---

Basic Try-Except Block

try:
    result = 10 / 0
except ZeroDivisionError:
    print("Cannot divide by zero!")

---

Catching Multiple Exceptions

try:
    x = int(input("Enter a number: "))
    result = 10 / x
except (ValueError, ZeroDivisionError) as e:
    print(f"Error occurred: {e}")

---

Using Else and Finally

• else block runs if no exceptions occur.

• finally block always runs, used for cleanup.

try:
    file = open("data.txt", "r")
    data = file.read()
except FileNotFoundError:
    print("File not found.")
else:
    print("File read successfully.")
finally:
    file.close()

---

Raising Exceptions

• You can raise exceptions manually using raise.

def check_age(age):
    if age < 0:
        raise ValueError("Age cannot be negative.")

check_age(-1)

---

Custom Exceptions

• Create your own exception classes by inheriting from Exception.

class MyError(Exception):
    pass

def do_something():
    raise MyError("Something went wrong!")

try:
    do_something()
except MyError as e:
    print(e)

---

Summary

• Use try-except to catch and handle errors.

• Use else and finally for additional control.

• Raise exceptions to signal errors.

• Define custom exceptions for specific needs.

---

#Python #ExceptionHandling #Errors #Debugging #ProgrammingTips

Topic: Python List vs Tuple — Differences and Use Cases

---

Key Differences

• Lists are mutable — you can change, add, or remove elements.

• Tuples are immutable — once created, they cannot be changed.

---

Creating Lists and Tuples

my_list = [1, 2, 3]
my_tuple = (1, 2, 3)

---

When to Use Each

• Use lists when you need a collection that can change over time.

• Use tuples when the collection should remain constant, providing safer and faster data handling.

---

Common Tuple Uses

• Returning multiple values from a function.

def get_coordinates():
    return (10, 20)

x, y = get_coordinates()

• Using as keys in dictionaries (since tuples are hashable, lists are not).

---

Converting Between Lists and Tuples

list_to_tuple = tuple(my_list)
tuple_to_list = list(my_tuple)

---

Performance Considerations

• Tuples are slightly faster than lists due to immutability.

---

Summary

• Lists: mutable, dynamic collections.

• Tuples: immutable, fixed collections.

• Choose based on whether data should change or stay constant.

---

#Python #Lists #Tuples #DataStructures #ProgrammingTips

https://news.1rj.ru/str/DataScience4

Topic: Mastering Recursion — From Basics to Advanced Applications

---

What is Recursion?

• Recursion is a technique where a function calls itself to solve smaller instances of a problem until reaching a base case.

---

Basic Structure

• Every recursive function needs:

* A base case to stop recursion.

* A recursive case that breaks the problem into smaller parts.

---

Simple Example: Fibonacci Numbers

def fibonacci(n):
    if n <= 1:
        return n  # base case
    else:
        return fibonacci(n-1) + fibonacci(n-2)  # recursive case

---

Drawbacks of Naive Recursion

• Repeated calculations cause exponential time complexity.

• Can cause stack overflow on large inputs.

---

Improving Recursion: Memoization

• Store results of subproblems to avoid repeated work.

memo = {}
def fib_memo(n):
    if n in memo:
        return memo[n]
    if n <= 1:
        memo[n] = n
    else:
        memo[n] = fib_memo(n-1) + fib_memo(n-2)
    return memo[n]

---

Advanced Concepts

• Tail Recursion: Recursive call is the last operation. Python does not optimize tail calls but understanding it is important.

• Divide and Conquer Algorithms: Recursion breaks problems into subproblems (e.g., Merge Sort, Quick Sort).

---

Example: Merge Sort

def merge_sort(arr):
    if len(arr) <= 1:
        return arr

    mid = len(arr) // 2
    left = merge_sort(arr[:mid])
    right = merge_sort(arr[mid:])

    return merge(left, right)

def merge(left, right):
    result = []
    i = j = 0

    while i < len(left) and j < len(right):
        if left[i] < right[j]:
            result.append(left[i])
            i += 1
        else:
            result.append(right[j])
            j += 1

    result.extend(left[i:])
    result.extend(right[j:])
    return result

---

Exercise

• Implement a recursive function to solve the Tower of Hanoi problem for *n* disks and print the moves.

---

#Algorithms #Recursion #Memoization #DivideAndConquer #CodingExercise

https://news.1rj.ru/str/DataScience4

Topic: Python File Handling — Reading, Writing, and Managing Files (Beginner to Advanced)

---

What is File Handling?

• File handling allows Python programs to read from and write to external files — such as .txt, .csv, .json, etc.

• Python uses built-in functions like open(), read(), and write() to interact with files.

---

Opening a File

file = open("example.txt", "r")  # "r" = read mode
content = file.read()
file.close()

---

Using with Statement (Best Practice)

• Automatically handles file closing:

with open("example.txt", "r") as file:
    content = file.read()

---

File Modes

• "r" — read (default)
• "w" — write (creates or overwrites)
• "a" — append (adds to the end)
• "x" — create (fails if file exists)
• "b" — binary mode
• "t" — text mode (default)

---

Writing to Files

with open("output.txt", "w") as file:
    file.write("Hello, world!")

• Note: "w" overwrites existing content.

---

Appending to Files

with open("output.txt", "a") as file:
    file.write("\nNew line added.")

---

Reading Line by Line

with open("example.txt", "r") as file:
    for line in file:
        print(line.strip())

---

Working with File Paths

• Use os.path or pathlib for platform-independent paths.

from pathlib import Path

file_path = Path("folder") / "file.txt"
with open(file_path, "r") as f:
    print(f.read())

---

Advanced Tip: Reading and Writing CSV Files

import csv

with open("data.csv", "w", newline="") as file:
    writer = csv.writer(file)
    writer.writerow(["name", "age"])
    writer.writerow(["Alice", 30])

with open("data.csv", "r") as file:
    reader = csv.reader(file)
    for row in reader:
        print(row)

---

Summary

• Use open() with correct mode to read/write files.

• Prefer with statement to manage files safely.

• Use libraries like csv, json, or pickle for structured data.

• Always handle exceptions like FileNotFoundError for robust file operations.

---

Exercise

• Write a Python program that reads a list of names from names.txt, sorts them alphabetically, and saves the result in sorted_names.txt.

---

#Python #FileHandling #ReadWrite #DataProcessing #ProgrammingTips

https://news.1rj.ru/str/DataScience4

Topic: Django Models and ORM — From Basics to Advanced Queries

---

What is a Model in Django?

• A model in Django is a Python class that defines the structure of your database table. Each model maps to a table, and each attribute represents a column.

• Django uses its ORM (Object-Relational Mapping) to interact with the database using Python code instead of SQL.

---

Creating Your First Model

from django.db import models

class Book(models.Model):
    noscript = models.CharField(max_length=200)
    author = models.CharField(max_length=100)
    published_date = models.DateField()
    pages = models.IntegerField()

---

Making Migrations

• Create and apply migrations to sync models with the database:

python manage.py makemigrations
python manage.py migrate

---

Using the Model

# Creating a new record
book = Book(noscript="1984", author="George Orwell", published_date="1949-06-08", pages=328)
book.save()

# Fetching all books
books = Book.objects.all()

# Filtering
orwell_books = Book.objects.filter(author="George Orwell")

# Getting one object
book = Book.objects.get(id=1)

# Updating
book.noscript = "Animal Farm"
book.save()

# Deleting
book.delete()

---

Model Field Types

• CharField, TextField, IntegerField, FloatField, DateField, DateTimeField, BooleanField, EmailField, and more.

---

Meta Class for Model Options

class Book(models.Model):
    noscript = models.CharField(max_length=200)

    class Meta:
        ordering = ['noscript']  # default ordering by noscript

---

Relationships Between Models

• One-to-Many (ForeignKey)
• Many-to-Many (ManyToManyField)
• One-to-One (OneToOneField)

class Author(models.Model):
    name = models.CharField(max_length=100)

class Book(models.Model):
    noscript = models.CharField(max_length=200)
    author = models.ForeignKey(Author, on_delete=models.CASCADE)

---

Advanced ORM Queries

# Complex filters
books = Book.objects.filter(published_date__year__gte=2000, pages__lte=300)

# Exclude
books = Book.objects.exclude(author="J.K. Rowling")

# Ordering
books = Book.objects.order_by("-published_date")

# Count
total = Book.objects.count()

---

Summary

• Django models define your database structure.

• The ORM allows you to query and manipulate data using Python.

• Supports relationships, complex filtering, ordering, and aggregation.

---

Exercise

• Create two models: Author and Book. Link them using a foreign key. Then, write views that:

1. Add a new book.
2. List all books by a specific author.
3. Delete books published before the year 2000.

---

#Django #WebDevelopment #ORM #DatabaseModels #DjangoTips

https://news.1rj.ru/str/DataScience4